Parameter estimation of cyclostationary AM time series withapplication to missing observations

Time series with systematic misses occur often in practice and can be modeled as amplitude modulated ARMA processes. With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in the paper. Assuming that the modulating sequence is (almost) periodic, parameter estimation algorithms are developed based on second- and higher order cumulants of the resulting cyclostationary observations, which may be corrupted by any additive stationary noise of unknown covariance. If unknown, the modulating sequence can be recovered even in the presence of additive (perhaps nonstationary and colored) Gaussian, or any symmetrically distributed, noise. If the ARMA process is nonGaussian, cyclic cumulants of order greater than three can identify (non)causal and (non)minimum phase models from partial noisy data. Simulation experiments corroborate the theoretical results     

A two-dimensional channel simulation model for shadowing processes

A Gaussian random process with a given power spectral density (PSD) function can be modeled as a sum of sinusoids (SOS), and has been widely used to simulate Rayleigh-fading communication channels. The conventional one-dimensional (1D) channel model cannot capture the spatial correlation of shadowing processes. We here develop a two-dimensional (2D) SOS-based channel model to simulate the shadowing process. Three methods to fit the PSD of the simulated process to the true channel's PSD are explored. Performance of the proposed channel simulator is analyzed in terms of the autocorrelation function of the simulated shadowing process. Simulations illustrate the potential of the proposed channel simulation model.     

Estimation and detection in non-Gaussian noise using higher orderstatistics

One of the primary applications of higher order statistics has been for detection and estimation of nonGaussian signals in Gaussian noise of unknown covariance. This is motivated by the fact that higher order cumulants of Gaussian processes vanish. We study the opposite problem, namely, detection and estimation in nonGaussian noise. We estimate cumulants of nonGaussian processes in the presence of unknown deterministic and/or Gaussian signals, which allows either parametric or nonparametric estimation of the covariance of the nonGaussian noise. Our approach is to augment existing second-order detection methods using cumulants. We propose solutions for detection of deterministic signals based on matched filters and the generalized likelihood ratio test which incorporate cumulants, where the resulting solutions are valid under either detection hypotheses. This allows for single record detection and obviates the need for noise-only training records. The problem of estimating signal strength in the presence of nonGaussian noise of unknown covariance is also considered, and a cumulant-based solution is proposed which uses a single data record. Examples are used throughout to illustrate our proposed methods     

Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices

Face recognition on mobile devices, such as personal digital assistants and cell phones, is a big challenge owing to the limited computational resources available to run verifications on the devices themselves. One approach is to transmit the captured face images by use of the cell-phone connection and to run the verification on a remote station. However, owing to limitations in communication bandwidth, it may be necessary to transmit a compressed version of the image. We propose using the image compression standard JPEG2000, which is a wavelet-based compression engine used to compress the face images to low bit rates suitable for transmission over low-bandwidth communication channels. At the receiver end, the face images are reconstructed with a JPEG2000 decoder and are fed into the verification engine. We explore how advanced correlation filters, such as the minimum average correlation energy filter [Appl. Opt. 26, 3633 (1987)] and its variants, perform by using face images captured under different illumination conditions and encoded with different bit rates under the JPEG2000 wavelet-encoding standard. We evaluate the performance of these filters by using illumination variations from the Carnegie Mellon University's Pose, Illumination, and Expression (PIE) face database. We also demonstrate the tolerance of these filters to noisy versions of images with illumination variations.     

Multichannel ARMA processes

Parametric modeling of multichannel time series is accomplished by using higher (than second) order statistics (HOS) of the observed nonGaussian data. Cumulants of vector processes are defined using a Kronecker product formulation, and consistency of their sample estimators is addressed. Identifiability results in connection with the HOS-based parameter estimation of causal and noncausal multivariate ARMA processes are established. Estimates of the parameters of causal ARMA models are obtained as the solution to a set of linear equations, whereas those of noncausal ARMA models are obtained as the solution to a cumulant matching algorithm. Conventional approaches based on second-order statistics can identify a multichannel system only to within post multiplication by a unimodular matrix. HOS-based methods yield solutions that are unique to within post-multiplication by an (extended) permutation matrix; additionally, the multiminimum phase assumption can be relaxed, and the observations may be contaminated with colored Gaussian noise. Frequency-domain methods for nonparametric system identification are discussed briefly. Simulations results validating the multichannel parameter estimation algorithms are provided     

Ein Lichtmast aus Glas für die Beleuchtung des Marktplatzes in Göppingen

A lighting mast made of glass for the market place in Goeppingen. The paper presents the design and construction details of glass – lighting masts, whose prototype was developed for the shadowless lighting of the market place in Goeppingen. The cross‐shaped mast consists of a 2.0 m high plate‐base made of stainless‐steel and of a 6.0 m high glass column, made of four wings of composite safety glass, screwed on the top of it. The wings are connected to a centrally placed steel core in such a way, that the structure provides in a catastrophe case the necessary residual bearing capacity. Four multifaceted mirrors, placed at a height of 8.0 m, reflect the light‐beams that are projected by projectors, situated at the ground.     

Modeling survival of Listeria monocytogenes in the traditional Greek soft cheese Katiki

In the present work, survival of Listeria monocytogenes in the traditional Greek soft, spreadable cheese Katiki was studied throughout the shelf life of the product. Samples of finished cheese were inoculated with a cocktail of five L. monocytogenes strains (ca. 6 log CFU g(-1)) and stored at 5, 10, 15, and 20 degrees C. Acid-stress adaptation or cross-protection to the same stress was also investigated by inoculation of acid-adapted cells in the product. The results showed that pathogen survival was biphasic. Various mathematical equations (Geeraerd, Cerf, Albert-Mafart, Whiting, Zwietering, and Baranyi models) were fitted to the experimental data. A thorough statistical analysis was performed to choose the best model. The Geeraerd model was finally selected, and the results revealed no acid tolerance acquisition (no significant differences, P > 0.05, in the survival rates of the non-acid-adapted and acid-adapted cells). Secondary modeling (second-order polynomial with a(0) = 0.8453, a(1) = -0.0743, and a(2) = 0.0059) of the survival rate (of sensitive population), and other parameters that were similar at all temperatures (fraction of initial population in the major population = 99.98%, survival rate of resistant population = 0.10 day(-1), and initial population = 6.29 log CFU g(-1)), showed that survival of the pathogen was temperature dependent with bacterial cells surviving for a longer period of time at lower temperatures. Finally, the developed predictive model was successfully validated at two independent temperatures (12 and 17 degrees C). This study underlines the usefulness of predictive modeling as a tool for realistic estimation and control of L. monocytogenes risk in food products. Such data are also useful when conducting risk assessment studies.     

Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

International Journal of Computer Vision - This paper presents a novel subject-dependent deep aging path (SDAP), which inherits the merits of both generative probabilistic modeling and inverse...     

Fault‐Tolerant Control Allocation for Overactuated Nonlinear Systems

This paper addresses the problem of fault‐tolerant control allocation for input affine nonlinear systems. The proposed scheme is divided in three main tasks: fault detection and estimation using a nonlinear observer, fault isolation through a bank of unknown input observers with a resetting policy to reduce the effects of nonlinearities and control reconfiguration based on reduced order allocation. Analytical results regarding the isolability and reconfigurability of actuator faults are derived and a simulation example is used to illustrate the the proposed fault tolerant control methodology.